Soft Computing: Fundamentals and Applications , Revised Edition
Author(s): D. K. Pratihar
E-ISBN: Publication Year: Reprint 2018
Binding: Paper Back Dimension: 185mm x 240mm Weight: 460
About the book
SOFT COMPUTING: Fundamentals and Applications starts with an introduction to soft computing, a family consists of many members, namely Genetic Algorithms (GAs), Fuzzy Logic (FL), Neural Networks (NNs), and others. To realize the need for a non-traditional optimization tool like GA, one chapter is devoted to explain the principle of traditional optimization. The working cycle of a GA is explained in detail. The mechanisms of some specialized GAs are then discussed with some appropriate examples. The working principles of some other non-traditional optimization tools like Simulated Annealing (SA) and Particle Swarm Optimization (PSO) are discussed in detail. Multi-objective optimization has been dealt in a separate chapter, where the working principles of a few approaches are explained. Fuzzy sets are introduced before explaining the principle of fuzzy reasoning and clustering. The fundamentals of NNs are presented, prior to the discussion on various forms of NN. The combined techniques, such as GA-FL, GA-NN, NN-FL and GA-FL-NN are then explained, and the last chapter deals with the applications of soft computing in two different fields of research.
It has been written to fulfill the requirements of a large number of readers belonging to various disciplines of engineering and general sciences. The algorithms are discussed with a number of solved numerical examples. It will be very much helpful to the students, scientists and practicing engineers.
Table of Contents
Dedication / Preface / Nomenclature / Greek Symbols / Abbreviations / Introduction / Optimization and Some Traditional Methods / Introduction to Genetic Algorithms / Some Specialized Genetic Algorithms / Overview of Other Non-Traditional Optimization Methods / Multi-Objective Optimization / Introduction to Fuzzy Sets / Fuzzy Reasoning and Clustering / Fundamentals of Neural Networks / Some Examples of Neural Networks / Combined Genetic Algorithms: Fuzzy Logic / Combined Genetic Algorithms: Neural Networks / Combined Neural Networks: Fuzzy Logic / Applications of Soft Computing.